Analysis of home electricity usage reveals how much people charge – and therefore drive – their EVs.
It’s hard to overstate how different the vehicle market will be in 2035 if automakers like General Motors and Volvo stick to their plans to produce mostly electric vehicles (EVs) and stop selling gasoline-powered cars. EVs have been anointed as the replacement technology, but have a long way to go, comprising less than 1% of the vehicle fleet today.
Meeting these lofty goals without imposing serious costs on consumers will require EVs to serve as close substitutes for gasoline cars. But is this the case so far? That’s an important question to answer as California leads the way on this transition and the Biden administration signals its intention to make the shift to EVs nationally. Yet the truth is we are far from understanding how much it will cost to fully transition to EVs.
One way to start to answer this question is to look at how much people are driving their EVs. If households are putting as many miles on their EVs as on their gas-guzzling counterparts, this is a good sign for the EV revolution. On the other hand, if households only drive their EVs sparingly, it might be an indication that current EV technology simply isn’t as good. That could signal more innovation will be needed before consumers will make that switch en masse, or else the switch away from gasoline could be more costly than we hope.
Despite the importance of this question, it turns out to be surprisingly hard to gather comprehensive data on how much EVs are driven (something we have lamented before!). Some researchers have used survey evidence, but these results come from people who are particularly excited to talk about their new EVs and may not represent average drivers. Even California policymakers are choosing to rely on just a few hundred households that have installed a dedicated electricity meter for their EVs to guess the behavior of hundreds of thousands of EV drivers. These meters are costly to install, so the households that install them probably don’t represent average drivers. The financial stakes for the economy are real: these estimates of EV electricity consumption feed into infrastructure planning and determine how Low Carbon Fuel Standard credits are allocated.
Measuring electric vehicle miles traveled
In a new working paper (which we will be presenting at next month’s Energy Institute POWER Conference), we provide the first at-scale estimates of “electric vehicle miles traveled” (eVMT) that represent the overall EV population in California. We combine nearly 12 billion hours of electricity meter measurements—a representative sample of roughly 10% of residential electricity meters in California’s largest utility, Pacific Gas & Electric—with household-level EV registration records from 2014 to 2017 to estimate how much EV charging is occurring at home. We then adjust this result to account for away-from-home charging to retrieve a measure of average eVMT.
We find that when a household gets a new EV, electricity demand increases sharply, and then stays at this higher level. However, the increase we see is less than half the amount state regulators have assumed based on households with dedicated meters: only 2.9 kilowatt-hours (kWh) per day. That translates into about 5,300 miles of driving per year—roughly half as far as regulators’ estimates, and only half as far as people on average drive their gas-powered cars. Lucas found something similar using data from a nationally representative survey, although he’s looking at total miles driven, not eVMT, a distinction that matters for plug-in hybrid electric vehicles.
All told, this suggests that households may not yet view EVs as a good substitute for their gasoline-powered cars and that, unless there are major improvements in EV technology, regulators and policy-makers have more work to do to convince drivers to abandon their gasoline-powered cars for EVs.
Can this be right?
Our estimate has been reported on recently (see, e.g., here, here and here) and a number of people have raised questions about the results. We were surprised by our findings as well, and have taken a number of steps to stress-test them. Here are some of the details behind our estimates:
- But, EV owners also charge away from home. This is important, and we account for it in our estimates. We start by estimating the change in residential electricity use when households install EVs. We convert these estimates into eVMT in two steps: (i) we apply a fuel efficiency conversion (to go from home kWh to home-charged eVMT), which accounts for the fact that 1 kWh translates to different amounts of eVMT across models; and (ii) we use aggregate data on non-residential charging from the California Air Resource Board’s (CARB’s) Low Carbon Fuel Standard (LCFS) program to estimate out-of-home charging. CARB’s data include all nonresidential metered charging that earns an LCFS credit. Charging providers have a strong incentive to report to CARB: these LCFS credits are worth approximately $0.20 – $0.25 per kWh. Thus, we expect these data to cover the bulk of non-residential charging (including, for example, Tesla’s Supercharger network). Non-metered charging is necessarily excluded from these data. Even if non-metered charging were to make up 10% of all non-residential charging, however, our overall annual eVMT estimate would remain under 5,500. Using the CARB data, we calculate that EVs drive around 5,300 eVMT per year. We estimate that 75% of this, or approximately 3,975 eVMT come from at-home charging, and 25% or 1,325 eVMT come from out-of-home charging.
- But, a lot of EV owners have rooftop solar. It is important to account for solar PV, as approximately 20% of the EV owners in our sample also have solar panels. We observe whether each household in our sample has a solar interconnection and when this interconnection occurred. We find that installing solar PV reduces a household’s (net) kWh consumed by 0.8 kWh per hour on average. We see that this reduction occurs during daytime hours only, giving us confidence that this control is working properly and that we’re accounting for rooftop solar in our estimates.
- But, all EVs are not created equally. We recognize this and estimate home charging for three distinct groups of EVs: Teslas, non-Tesla battery-electric vehicles (like the Nissan Leaf and Chevy Bolts), and plug-in hybrid electric vehicles (like the Ford C-Max). Teslas increase household load by 0.24 kWh per hour on average. The non-Tesla battery electric vehicles like Leafs increase load by 0.10 kWh per hour, and the plug-in hybrids like the C-Max increase load by 0.09 kWh per hour. This reflects both the fact that Teslas consume more electricity per eVMT than the other battery electric vehicles and that Teslas in our sample are driven further than other vehicles off of home charging. We account for these differences – and the composition of vehicle types in our sample – when we calculate the average eVMT in the sample.
- But, things have changed since 2017. This is certainly true, and there are reasons to believe that people who bought EVs over the last 3 years may drive them more than people who bought EVs from 2014-2017 (e.g., the newer vehicles have longer ranges, and people who drive a lot and were worried about range waited to buy their EVs). But, we do not see any detectable changes in our results from 2014 to 2017, and some of the same factors were at play over this time period. This makes us think that newer data might not be dramatically different, but we don’t know.
What does this mean for policy?
Whatever the explanation for the lower EV miles driven, there are clear lessons for policymakers. First, EV manufacturers should be required to make eVMT data available to regulators and researchers, so that our results can be replicated in other settings. We spent years gaining access to the data for this study – but this process could be easier. EV manufacturers digitally record data from the cars that they sell – but they haven’t been required to share the information and they have little to no incentive to share it voluntarily.
Even utilities don’t know how much power is used by the cars in their service territories. Meanwhile, they are spending hundreds of millions of dollars upgrading electricity charging and distribution infrastructure and the companies making these investments, and the regulators approving them, have limited information about where the cars are, let alone how much electricity they are using. For example, in California, revisions to the Low Carbon Fuel Standard allow vehicle manufacturers to claim “incremental” credits for the electricity their cars use, but these regulations are set up in ways that continue to keep key decision makers out of the loop. We would support rules that require the car manufacturers to report usage data to all of the relevant government agencies. This could be made a condition for qualifying for publicly supported incentives. We also believe that agencies should be able to share such data with researchers (under confidentiality arrangements) who will perform analyses that are critical to improve EV policy.
Second, much more policy innovation is needed to move 100% of road travel to electric. Rather than relying on bans and mandates to sell EVs, we could try giving drivers the right incentives. Pricing vehicle emissions would be a good start. At the moment, incentives are backwards. Electricity prices in the United States are low where the grid is dirtiest and high where the grid is cleanest. Some EV owners in California pay several times more to charge than their neighbors due to the vagaries of utility service territory boundaries. This is both inefficient and unfair.
Collectively, we are only beginning to learn some of the most basic facts about the costs and benefits of transportation electrification. To inform efficient policy decisions going forward, we must democratize access to key data sources (like eVMT and charging behavior), acknowledge the fact that there is much we do not yet know, and create conditions that allow us to course-correct as new information becomes available.
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Suggested citation: Burlig, Fiona, Bushnell, James, Rapson, David and Wolfram, Catherine. “Electric Vehicle Owners Drive Less Than We Thought” Energy Institute Blog, UC Berkeley, February 16, 2021, https://energyathaas.wordpress.com/2021/02/16/electric-vehicle-owners-drive-less-than-we-thought/
Catherine Wolfram is Associate Dean for Academic Affairs and the Cora Jane Flood Professor of Business Administration at the Haas School of Business, University of California, Berkeley. She is the Program Director of the National Bureau of Economic Research's Environment and Energy Economics Program, Faculty Director of The E2e Project, a research organization focused on energy efficiency and a research affiliate at the Energy Institute at Haas. She is also an affiliated faculty member of in the Agriculture and Resource Economics department and the Energy and Resources Group at Berkeley.
Wolfram has published extensively on the economics of energy markets. Her work has analyzed rural electrification programs in the developing world, energy efficiency programs in the US, the effects of environmental regulation on energy markets and the impact of privatization and restructuring in the US and UK. She is currently implementing several randomized controlled trials to evaluate energy programs in the U.S., Ghana, and Kenya.
She received a PhD in Economics from MIT in 1996 and an AB from Harvard in 1989. Before joining the faculty at UC Berkeley, she was an Assistant Professor of Economics at Harvard.